scholarly journals Probabilistic Inference: Task Dependency and Individual Differences of Probability Weighting Revealed by Hierarchical Bayesian Modeling

2016 ◽  
Vol 7 ◽  
Author(s):  
Moritz Boos ◽  
Caroline Seer ◽  
Florian Lange ◽  
Bruno Kopp
Keyword(s):  
Individual Differences ◽  
Bayesian Modeling ◽  
Probabilistic Inference ◽  
Probability Weighting ◽  
Hierarchical Bayesian ◽  
Hierarchical Bayesian Modeling ◽  
Task Dependency ◽  
Inference Task

scholarly journals Individual differences in attention strategies during detection, fine discrimination, and coarse discrimination

2013 ◽  
Vol 110 (3) ◽  
pp. 784-794 ◽  
Author(s):  
David A. Bridwell ◽  
Elizabeth A. Hecker ◽  
John T. Serences ◽  
Ramesh Srinivasan
Keyword(s):  
Individual Differences ◽  
Bayesian Modeling ◽  
Spatial Location ◽  
Visual Features ◽  
Behavioral Performance ◽  
Visual Responses ◽  
Target Orientation ◽  
Tuning Curves ◽  
Hierarchical Bayesian Modeling ◽  
Families Of Curves

Interacting with the environment requires the ability to flexibly direct attention to relevant features. We examined the degree to which individuals attend to visual features within and across Detection, Fine Discrimination, and Coarse Discrimination tasks. Electroencephalographic (EEG) responses were measured to an unattended peripheral flickering (4 or 6 Hz) grating while individuals ( n = 33) attended to orientations that were offset by 0°, 10°, 20°, 30°, 40°, and 90° from the orientation of the unattended flicker. These unattended responses may be sensitive to attentional gain at the attended spatial location, since attention to features enhances early visual responses throughout the visual field. We found no significant differences in tuning curves across the three tasks in part due to individual differences in strategies. We sought to characterize individual attention strategies using hierarchical Bayesian modeling, which grouped individuals into families of curves that reflect attention to the physical target orientation (“on-channel”) or away from the target orientation (“off-channel”) or a uniform distribution of attention. The different curves were related to behavioral performance; individuals with “on-channel” curves had lower thresholds than individuals with uniform curves. Individuals with “off-channel” curves during Fine Discrimination additionally had lower thresholds than those assigned to uniform curves, highlighting the perceptual benefits of attending away from the physical target orientation during fine discriminations. Finally, we showed that a subset of individuals with optimal curves (“on-channel”) during Detection also demonstrated optimal curves (“off-channel”) during Fine Discrimination, indicating that a subset of individuals can modulate tuning optimally for detection and discrimination.

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